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Edge computing enabled smart greenhouse is a representative application ofInternet of Things technology, which can monitor the environmental informationin real time and employ the information to contribute to intelligentdecision-making. In the process, anomaly detection for wireless sensor dataplays an important role. However, traditional anomaly detection algorithmsoriginally designed for anomaly detection in static data have not properlyconsidered the inherent characteristics of data stream produced by wirelesssensor such as infiniteness, correlations and concept drift, which may pose aconsiderable challenge on anomaly detection based on data stream, and lead tolow detection accuracy and efficiency. First, data stream usually generatesquickly which means that it is infinite and enormous, so any traditionaloff-line anomaly detection algorithm that attempts to store the whole datasetor to scan the dataset multiple times for anomaly detection will run out ofmemory space. Second, there exist correlations among different data streams,which traditional algorithms hardly consider. Third, the underlying datageneration process or data distribution may change over time. Thus, traditionalanomaly detection algorithms with no model update will lose their effects.Considering these issues, a novel method (called DLSHiForest) on basis ofLocality-Sensitive Hashing and time window technique in this paper is proposedto solve these problems while achieving accurate and efficient detection.Comprehensive experiments are executed using real-world agricultural greenhousedataset to demonstrate the feasibility of our approach. Experimental resultsshow that our proposal is practicable in addressing challenges of traditionalanomaly detection while ensuring accuracy and efficiency.

Fast Wireless Sensor Anomaly Detection based on Data Stream in Edge Computing Enabled Smart Greenhouse